Replication Assignment Carly

Introduction

Summary of CSV Files/Terms

Add definitions for things like: - INI: - AR: - ONA

Loading Required Packages

suppressPackageStartupMessages({ #ignore this initial function (performing this to hide loading messages from running project)
  library(ggplot2)
  library(dplyr)
  library(datasets)
  library(RColorBrewer)
  library(tibble)
  library(ggpubr)
  library(gridExtra)
  library(FSA)
  library(ggthemes)
  if (!require("gplots", quietly = TRUE)) {
    install.packages("gplots", dependencies = TRUE)
  }
  library(gplots)
  if (!require("RColorBrewer", quietly = TRUE)) {
    install.packages("RColorBrewer", dependencies = TRUE)
  }
  library(RColorBrewer)
  library(dendextend)
  library(grid)
  library(gtable)
  library(ggprism)
  library(ggimage)
  library(png)
})

Special Case for “gganimate”:

# in order to install this final package, it's dependent on already having the packages "ggplot2", "gifski", and "transformr" installed
library(ggplot2)
library(gifski)
library(transformr)
library(gganimate)
library(patchwork)

Figure 1

Figure 1A

R was not used to generate figure 1A for the paper, I am inserting it here:

# loading the image (a screenshot i took)
image1a <- "~/Desktop/DATA/fig1A.png"
img <- readPNG(image1a) #reading my image in PNG format and converting it to an array/matrix

xhat <- 0.58   # adjusting for right/left movement of my circle
yhat <- 0.17   # adjusting for up/down movement of my circle

# plotting a red circle to overlay the image to identify the coordinate of interest
img1a <- ggplot() +
  annotation_custom(rasterGrob(img, width = unit(1, "npc"), height = unit(1, "npc"))) +
  geom_point(aes(x = xhat, y = yhat), color = "red", size = 16, shape = 1, stroke = 1.5) +  
  theme_void() +
  coord_fixed(xlim = c(0, 1.5), ylim = c(0, 1)) 
print(img1a)

🐋 Note about this figure

A Note: I somewhat cheated by inserting the screenshot provided by the initial research team — however, this figure is solely inserted to mimic the original paper’s figure one to the best of my ability.

Figure 1B

• Loading CSV File Used for Figure 1B: Perc_SE_songs
• This CSV contains all the percentages of each INI (inter-note-interval) type from 1999 to 2005 at the southeast (SE) location of the Oceanic Northeast Atlantic (ONA) region.

# Figure 1B: INI percentage song type in South East location from the ONA region
perc_SE <- read.csv(file="~/Desktop/DATA/Perc_SE_songs.csv", header=TRUE, dec=".")
str(perc_SE)

## 'data.frame':    18 obs. of  3 variables:
##  $ year: chr  "1998/1999" "1999/2000" "2000/2001" "2001/2002" ...
##  $ perc: num  100 100 91.3 0 20 ...
##  $ INI : chr  "19s" "19s" "19s" "19s" ...

perc_SE$INI <- factor(perc_SE$INI, levels = c("19s", "Hyb", "12s"))

# "Make sure perc and Rythm are in the correct format" - but this is wrong (Ry)
perc_SE$perc <- as.numeric(perc_SE$perc)
perc_SE$INI <- factor(perc_SE$INI) # no column in the file that has rhythm in it - changed original rhythm to INI?
# there could be a relationship between INI and rhythm in this context (they very well could represent the same thing)...however... i think that this should be clearer either in their readme file or in their actual methods outlining how they analyzed this portion of their study via R 
# why you might ask? because i spent 2 hours trying to troubleshoot using the "Rhythm" variable when it simply did not exist in the original CV file

perc <- ggplot(perc_SE, aes(fill=INI, y=perc, x=year)) +
  geom_bar(position="fill", stat="identity", width=0.5) +
  scale_y_continuous(name = "INI type (%)",labels = scales::percent_format()) +
  theme_bw()+
  theme(text=element_text(family = "Times"),panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        strip.text.x = element_text(size = 12, face="bold", margin = margin(0.5, 0, 0.5, 0, "cm")),
        strip.background = element_rect(fill="grey97"),
        axis.title = element_text(face="bold", size = 12),
        axis.title.x = element_blank(),
        axis.text.x = element_text(colour="black", size = 12, angle=60, vjust = 0.5, hjust=0.5),
        axis.text.y = element_text(colour="black", size = 12), legend.position = "top", 
        legend.text=element_text(size=10), legend.title=element_blank()) +
  labs(tag = "B") +
  scale_fill_manual(values=c("red","cyan", "springgreen4")) +
#adding annotation for dashed line in 2001/2002 column
  geom_segment(aes(x = which(perc_SE$year == "2001/2002")[1], 
                   xend = which(perc_SE$year == "2001/2002")[1], 
                   y = 0.01, yend = 0.99), 
               linetype = "dashed", color = "black") +

#adding annotation for "No Data" (attempting to center it)
  annotate("text", x = which(perc_SE$year == "2001/2002")[1], y = 0.5, 
           label = "No Data", family = "Times", angle = 90, vjust = -0.5, hjust = .5, size = 4)
print(perc) #printing the figure (sanity check)

Figure 1C

• Loading CSV File Used for Figure 1C: INI_ave_see_99_05.csv
• This CSV contains all INI’s (inter-note-intervals) averaged by song collected from 1999 to 2005 at the SE location of the ONA region.

bout9905 <- read.csv(file="~/Desktop/DATA/INI_ave_SE_99_05.csv", header=TRUE, dec=".")
bout9905$date <- as.Date(bout9905$date, format = "%d/%m/%Y")
str(bout9905$date)  # ensuring we now have the date format

##  Date[1:94], format: "1999-02-11" "1999-02-19" "1999-02-21" "1999-02-26" "1999-03-02" ...

head(bout9905$date) # printing data to see how it looks 

## [1] "1999-02-11" "1999-02-19" "1999-02-21" "1999-02-26" "1999-03-02"
## [6] "1999-03-08"

# also part of figure 1C (INI's collected from 1999 to 2005 from the ONA region)
ini_b_9905 <- ggplot(bout9905, aes(x = date, y = Ave_INI)) + # plotting date against average INI for the ONA region
  geom_point(size=2, aes(group=1), color="darkblue")+
  geom_errorbar(aes(ymin=Ave_INI-SD_INI, ymax=Ave_INI+SD_INI),width=2, color="darkblue") +
  scale_y_continuous(name = "Average song INI (s)",
                     limits=c(9, 20), guide = guide_prism_minor()) + theme_bw() +  labs(tag = "C") + # setting limits so that my figure doesn't look crazy
  scale_x_date(breaks="6 month", date_labels="%b %y")

# formatting axes titles and plot details
ini_b_9905 <- ini_b_9905 + theme(plot.title = element_text(size = 18, family = "Times", face = "bold"),
        text=element_text(family = "Times"),panel.grid.major.y = element_line(colour="black", linetype=2), 
        panel.grid.minor = element_blank(), panel.grid.major.x = element_line(colour="black", linetype=2), strip.text.x = element_text(size = 10, face="bold", margin = margin(0.5, 0, 0.5, 0, "cm")),
        strip.background = element_rect(fill="grey97"),
        axis.title = element_text(face="bold", size = 12),
        axis.title.x = element_blank(),
        axis.text.x = element_text(colour="black", size = 10, vjust=0.5, angle=90),
        axis.text.y = element_text(colour="black", size = 10), legend.position = "top", 
        legend.text=element_text(size=10), legend.title=element_blank())
print(ini_b_9905)

# arranging figure 1 sections A-C to compare to the actual study's figure 1
grid.arrange(
  arrangeGrob(img1a, perc, ncol = 2),  
  ini_b_9905,                          
  nrow = 2,
  heights = c(1, 0.8)  
)

🐋 CLICK HERE To View Figure One from the Paper for a Comparison

Figure 1 (A-C) As Shown in the Paper

paper_fig1 <- "~/Desktop/DATA/fig1paperversion.png"
paper_fig1 <- readPNG(paper_fig1) #reading my image in PNG format and converting it to an array/matrix
grid.raster(paper_fig1)

Comments Regarding Figure 1: - Researchers tried to run this model using a column title called “perc_SEdollarsignRythm”, which does not exist and is not found anywhere else in the above code. I renamed this to the column “perc_SEdollarsignINI”, which still properly mirrors the 1B figure in the original paper - I believe this was just a naming mishap that could have arisen from their end.

Figure 3

• Observing gradual changes in song INIs and notes frequencies

Figure 3A:

• Loading CSV File Used for Figure 1C: INI_ave_06_20.csv
• This CSV contains all contains all INI’s (inter-note-intervals) averaged by song collected from 2006 to 2020 and sorted by location, region and year.

# figure 3A: sorted INI gradient from 2006 to 2020 for all locations and regions
bout0620 <- read.csv(file="~/Desktop/DATA/INI_ave_06_20.csv", header=TRUE, dec=".")
bout0620$date <- as.Date(bout0620$date, format = "%d/%m/%Y")

ini_b_0620 <- ggplot(bout0620, aes(x = date, y = Ave_INI, color= Region)) +
  geom_point(size=2, aes(group=1))+
  geom_errorbar(aes(ymin=Ave_INI-SD_INI, ymax=Ave_INI+SD_INI),width=30) +
  scale_y_continuous(name = "Average song INI (s)",
                     limits=c(7.8, 21.5), guide = guide_prism_minor()) + 
  theme_bw() +  
  scale_x_date (breaks="1 year", date_labels="%Y")+
  theme(plot.title = element_text(size = 15, family = "Times", face = "bold"),
        text=element_text(family = "Times"),panel.grid.major.y = element_line(colour="black", linetype=2), 
        panel.grid.minor = element_blank(),panel.grid.major.x = element_line(colour="black", linetype=2),
        strip.text.x = element_text(size = 15, face="bold", margin = margin(0.5, 0, 0.5, 0, "cm")),
        strip.background = element_rect(fill="grey97"),
        axis.title.y = element_text(colour="black", size = 15, face="bold"),
        axis.title.x = element_blank(),
        axis.text.x = element_text(colour="black", size = 15, vjust=0.5, angle=90),
        axis.text.y = element_text(colour="black", size = 15), legend.position = "top", 
        legend.text=element_text(size=15), legend.title=element_blank()) +
  scale_color_manual(values=c("darkviolet","darkorange","green4", "black","turquoise","darkblue","red" )) +
  #facet_grid(~year3, scale="free_x")
  stat_smooth( method = "glm",aes(group=1), colour = "black", fill= "grey", size=0.5)

print(ini_b_0620)

## `geom_smooth()` using formula = 'y ~ x'

Figure 3B:

• Loading CSV File Used for Figure 1C: 20Hz_ave_EARs.csv
• This CSV contains all contains all INI’s (inter-note-intervals) averaged by song collected from 2006 to 2020 and sorted by location, region and year.

# figure 3B: gradient of the peak frequency of the 20Hz only for EAR data
EARs <- subset(bout0620, Location == "Azores" | Location == "Gorringe")
EARs <- read.csv(file = "~/Desktop/DATA/20Hz_ave_EARs.csv", header = TRUE, dec = ".")

EARs$date <- as.Date(EARs$date, format = "%d/%m/%Y")

freq20_b_EAR <- ggplot(EARs, aes(x = date, y = Ave_PF, color = Region)) +
  geom_point(size = 2, aes(group = 1)) +
  geom_errorbar(aes(ymin = Ave_PF - SD_PF, ymax = Ave_PF + SD_PF), width = 30) +
  scale_y_continuous(name = "20-Hz note peak frequency (Hz)", limits = c(18, 25)) +
  theme_bw() +
  scale_x_date(breaks = "1 year", date_labels = "%Y") +
  theme(
    plot.title = element_text(size = 18, family = "Times", face = "bold"),
    text = element_text(family = "Times"),
    panel.grid.major.y = element_line(colour = "black", linetype = 2),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_line(colour = "black", linetype = 2),
    strip.text.x = element_text(size = 18, face = "bold", margin = margin(0.5, 0, 0.5, 0, "cm")),
    strip.background = element_rect(fill = "grey97"),
    axis.title = element_text(face = "bold", size = 20),
    axis.title.x = element_blank(),
    axis.text.x = element_text(colour = "black", size = 20, vjust = 0.5, angle = 90),
    axis.text.y = element_text(colour = "black", size = 20),
    legend.position = "top",
    legend.text = element_text(size = 16),
    legend.title = element_blank()
  ) +
  scale_color_manual(values = c("red", "darkblue")) +
  stat_smooth(method = "glm", aes(group = 1), colour = "black", fill = "grey", linewidth = 0.5)

print(freq20_b_EAR)

## `geom_smooth()` using formula = 'y ~ x'

Figure 3C:

• Loading CSV File Used for Figure 1C: HF_ave.csv
• This CSV contains peak frequencies of the high frequency (HF) note averaged by song (also includes standard deviations sorted by location, region and year).

# figure 3C:
HF <- read.csv(file="~/Desktop/DATA/HF_ave.csv", header=TRUE, dec=".")
HF$date <- as.Date(HF$date, format = "%d/%m/%Y")

HF_b <- ggplot(HF, aes(x = date, y = Ave_PF, color= Region)) +
  geom_point(size=2, aes(group=1))+
  geom_errorbar(aes(ymin=Ave_PF-SD_PF, ymax=Ave_PF+SD_PF),width=30) +
  scale_y_continuous(name = "HF note peak frequency (Hz)",
                     limits=c(122, 138)) + 
  theme_bw() +  
  scale_x_date(breaks="1 year", date_labels="%Y") +
  theme(plot.title = element_text(size = 16, family = "Times", face = "bold"),
        text=element_text(family = "Times"),panel.grid.major.y = element_line(colour="black", linetype=2),
        panel.grid.minor = element_blank(),panel.grid.major.x = element_line(colour="black", linetype=2),
        strip.text.x = element_text(size = 12, face="bold", margin = margin(0.5, 0, 0.5, 0, "cm")),
        strip.background = element_rect(fill="grey97"),
        axis.title = element_text(face="bold", size = 12),
        axis.title.x = element_blank(),
        axis.text.x = element_text(colour="black", size = 12, vjust=0.5, angle=90),
        axis.text.y = element_text(colour="black", size = 12), legend.position = "top", 
        legend.text=element_text(size=12), legend.title=element_blank()) +
  scale_color_manual(values=c("darkviolet", "red", "green4","black","turquoise", "darkblue")) +
  #facet_grid(~year2, scale="free_x") 
  stat_smooth(method = "glm",aes(group=1), colour = "black", fill= "grey", lwd=0.5)

print(HF_b)

## `geom_smooth()` using formula = 'y ~ x'

CARLY - add labels for A-C

🐋 CLICK HERE To View Figure Three from the Paper for a Comparison

Figure 3 (A-C) As Shown in the Paper

paper_fig3 <- "~/Desktop/DATA/paperfig3.png"
paper_fig3 <- readPNG(paper_fig3) #reading my image in PNG format and converting it to an array/matrix
grid.raster(paper_fig3)

Comments Regarding Figure 3: - I didn’t run into any errors with trying to run the code to generate these figures and the repetition for running these three made it easier for me to base 3B and 3C directly from the code that I generated for Figure 3A. - Still working through how to change the width of my plots within R Markdown but overall I am very happy with how these figures compare to those in the paper itself!

Supplemental Figure 1

Linear Models / Model Validation

• I went though and performed really simple linear regression using the lm() function

# model validation figures (figure 3 - figure supplement 1)

fig_3_1 <- par(mfrow = c(3, 3))

INI_lm <- read.csv(file="~/Desktop/DATA/INI_ave_06_20.csv", header=TRUE, dec=".")
LM_ini <- lm(Ave_INI ~ year, data=INI_lm)

# spend x residuals plot
plot(LM_ini$resid~INI_lm$Ave_INI[order(INI_lm$Ave_INI)],
     main="Internote intervals(INIs)x Residuals\nfor Simple Regression",
     xlab="INIs", ylab="Residuals")
abline(h=0,lty=2)

# histogram of Residuals
hist(LM_ini$resid, main="Histogram of Residuals", xlab="Residuals",
     ylab="Observations")

# Q-Q Plot
qqnorm(LM_ini$resid)
qqline(LM_ini$resid)

# repeating this process for 20 Hz note frequencies
EAR <- read.csv(file="~/Desktop/DATA/20Hz_ave_EARs.csv", header=TRUE, dec=".")
LM_20_EAR <- lm(Ave_PF ~ year, data=EAR)
summary(LM_20_EAR)

## 
## Call:
## lm(formula = Ave_PF ~ year, data = EAR)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.29695 -0.43743 -0.04964  0.34277  1.61369 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 151.87935   29.07868   5.223 6.74e-07 ***
## year         -0.06466    0.01444  -4.477 1.63e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6264 on 131 degrees of freedom
## Multiple R-squared:  0.1327, Adjusted R-squared:  0.1261 
## F-statistic: 20.04 on 1 and 131 DF,  p-value: 1.631e-05

# spend x residuals plot
plot(LM_20_EAR$resid ~ EAR$Ave_PF[order(EAR$Ave_PF)],
     main="20-Hz note peak frequency x Residuals\nfor Simple Regression",
     xlab="INIs", ylab="Residuals")
abline(h=0,lty=2)

# histogram of residuals
hist(LM_20_EAR$resid, main="Histogram of Residuals", xlab="Residuals",
     ylab="Observations")

# Q-Q Plot
qqnorm(LM_20_EAR$resid)
qqline(LM_20_EAR$resid)

HF_lm <- read.csv(file="~/Desktop/DATA/HF_ave.csv", header=TRUE, dec=".")
HF_LM <- lm(Ave_PF ~ year, data=HF_lm)

plot(HF_LM$resid~HF_lm$Ave_PF[order(HF_lm$Ave_PF)], #originally had this as Ave_peak_frequency - this is not the column title in their CV (CV column= Ave_PF)
     main="HF note peak frequency x Residuals\nfor Simple Regression",
     xlab="INIs", ylab="Residuals")
abline(h=0,lty=2)

# Histogram of Residuals
hist(HF_LM$resid, main="Histogram of Residuals", xlab="Residuals",
     ylab="Observations")

# Q-Q Plot
qqnorm(HF_LM$resid)
qqline(HF_LM$resid)

print(fig_3_1)

## $mfrow
## [1] 1 1

🐋 CLICK HERE for a Comparison to the Paper’s Figure

A Quick Look at the Original Paper’s Figure:

# paper's supplementary figure 3.1:
paper_plot <- "~/Desktop/DATA/figsup1paper.png"
paper_plot <- readPNG(paper_plot) #reading my image in PNG format and converting it to an array/matrix
grid.raster(paper_plot)

From how they both compare side by side, my plot vs. the plot featured in the paper appear very similar. I didn’t have a lot of issues with this step as it was just a combination of histograms, q-q plots, and regressions.

Figure 4: Histograms!

#Figure 4: Histograms of all INI measurements from the same singing season, for the actual individual graphs please click on the drop-down below this chunk!

ini_all<-read.csv(file="~/Desktop/DATA/INI_all.csv", header=TRUE, dec=".")

#2006/2007
ini0607<-subset(ini_all, year2=="2006/2007")
ini0607_g <- ggplot(ini0607, aes(INI, fill=Region)) +
  geom_histogram(color="black", alpha=0.5, bins=50) +
  theme_bw() +  
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        plot.title = element_text(size = 18, family = "Times", face = "bold"),
        text=element_text(family = "Times"),
        axis.title = element_text(face="bold", size = 12),
        axis.title.y = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x = element_text(colour="black", size = 12),
        axis.text.y = element_text(colour="black", size = 12), legend.position = "top", 
        legend.text=element_text(size=12), legend.title=element_blank()) +
  scale_x_continuous(limits=c(6,20)) +
  scale_fill_manual(values=c("turquoise", "darkblue"))

#2007/2008
ini0708<-subset(ini_all, year2=="2007/2008")
ini0708_g <- ggplot(ini0708, aes(INI, fill=Region)) +
  geom_histogram(color="black", alpha=0.5, bins=50) +
  theme_bw() +  
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        plot.title = element_text(size = 18, family = "Times", face = "bold"),
        text=element_text(family = "Times"),
        axis.title = element_text(face="bold", size = 20),
        axis.title.y = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x = element_text(colour="black", size = 12),
        axis.text.y = element_text(colour="black", size = 12), legend.position = "top", 
        legend.text=element_text(size=12), legend.title=element_blank()) +
  scale_x_continuous(limits=c(6,20)) +
  scale_fill_manual(values=c("black", "darkblue","red"))

#2014/2015
ini1415<-subset(ini_all, year2=="2014/2015")
ini1415_g <- ggplot(ini1415, aes(INI, fill=Region)) +
  geom_histogram(color="black", alpha=0.5, bins=30) + 
  theme_bw() +  
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        plot.title = element_text(size = 18, family = "Times", face = "bold"),
        text=element_text(family = "Times"),
        axis.title = element_text(face="bold", size = 20),
        axis.title.y = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x = element_text(colour="black", size = 12),
        axis.text.y = element_text(colour="black", size = 12), legend.position = "top", 
        legend.text=element_text(size=12), legend.title=element_blank()) +
  scale_fill_manual(values=c("darkviolet", "darkorange")) +
  scale_x_continuous(limits=c(6,20)) +
  scale_y_continuous(name = "Count")

#2015/2016
ini1516<-subset(ini_all, year2=="2015/2016")
ini1516_g <- ggplot(ini1516, aes(INI, fill=Region)) +
  geom_histogram(color="black", alpha=0.5, bins=50) +
  theme_bw() +  
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        plot.title = element_text(size = 18, family = "Times", face = "bold"),
        text=element_text(family = "Times"),
        axis.title = element_text(face="bold", size = 20),
        axis.title.y = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x = element_text(colour="black", size = 12, vjust=0.5),
        axis.text.y = element_text(colour="black", size = 12), legend.position = "top", 
        legend.text=element_text(size=12), legend.title=element_blank()) +
  scale_x_continuous(limits=c(6,20)) + 
  scale_fill_manual(values=c("darkviolet", "green4", "red"))

#2016/2017
ini1617<-subset(ini_all, year2=="2016/2017")
ini1617_g <- ggplot(ini1617, aes(INI, fill=Region)) +
  geom_histogram(color="black", alpha=0.5, bins=50) +
  theme_bw() +  
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        plot.title = element_text(size = 18, family = "Times", face = "bold"),
        text=element_text(family = "Times"),
        axis.title = element_text(face="bold", size = 20),
        axis.title.y = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x = element_text(colour="black", size = 12, vjust=0.5),
        axis.text.y = element_text(colour="black", size = 12), legend.position = "top", 
        legend.text=element_text(size=12), legend.title=element_blank()) +
  scale_x_continuous(limits=c(6,20)) +
  scale_fill_manual(values=c("green4", "darkblue"))

#2017/2018
ini1718<-subset(ini_all, year2=="2017/2018")
ini1718_g <- ggplot(ini1718, aes(INI, fill=Region)) +
  geom_histogram(color="black", alpha=0.5, bins=50) +
  theme_bw() +  
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        plot.title = element_text(size = 18, family = "Times", face = "bold"),
        text=element_text(family = "Times"),
        axis.title = element_text(face="bold", size = 20),
        axis.title.y = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x = element_text(colour="black", size = 12),
        axis.text.y = element_text(colour="black", size = 12), legend.position = "top", 
        legend.text=element_text(size=12), legend.title=element_blank()) +
  scale_x_continuous(limits=c(6,20)) +
  scale_fill_manual(values=c( "darkviolet", "darkblue"))

🐋 CLICK HERE To View The Individual Histograms

Figure 4 Histograms:

print(ini0607_g) #2006/2007

print(ini0708_g) #2007/2008

print(ini1415_g) #2014/2015

print(ini1516_g) #2015/2016

print(ini1617_g) #2016/2017

print(ini1718_g) #2017/2018

Combining All Histograms Together In Single Plot

ggarrange(ini0607_g, ini0708_g, ini1415_g, ini1516_g, ini1617_g, ini1718_g,
          ncol = 2, nrow = 3,
          align = "v",  # allows you to align this vertically
          labels = NULL,
          heights = c(1, 1, 1))

🐋 CLICK HERE To View Figure 4B’s Histograms

#Fig 4B: histograms of all peak frequencies of the HF note from the same singing season
hf_all <- read.csv(file="~/Desktop/DATA/HF_all.csv", header=TRUE, dec=".")

#2015/2016
hf1516 <- subset(hf_all, year2=="2015/2016") #wrong column labeled in original datafile

hf1516_g <- ggplot(hf1516, aes(Peak_Freq, fill=Region)) +
  geom_histogram(color="black", alpha=0.5, bins=18) +
  theme_bw() +  
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        plot.title = element_text(size = 18, family = "Times", face = "bold"),
        text=element_text(family = "Times"),
        axis.title = element_text(face="bold", size = 20),
        axis.title.y = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x = element_text(colour="black", size = 20),
        axis.text.y = element_text(colour="black", size = 20), legend.position = "none", 
        legend.text=element_text(size=20), legend.title=element_blank()) +
  scale_x_continuous(limits=c(120,135)) +
  scale_fill_manual(values=c("darkviolet","red","green4"))
hf1516_g

#2016/2017
hf1617 <- subset(hf_all, year2=="2016/2017")
hf1617_g <- ggplot(hf1617, aes(Peak_Freq, fill=Region)) +
  geom_histogram(color="black", alpha=0.5, bins=15) +
  theme_bw() +  
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        plot.title = element_text(size = 18, family = "Times", face = "bold"),
        text=element_text(family = "Times"),
        axis.title = element_text(face="bold", size = 20),
        axis.title.y = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x = element_text(colour="black", size = 20, vjust=0.5),
        axis.text.y = element_text(colour="black", size = 20), legend.position = "none", 
        legend.text=element_text(size=20), legend.title=element_blank()) +
  scale_x_continuous(limits=c(120,135)) +
  scale_fill_manual(values=c("green4", "blue"))
hf1617_g

#2017/2018
hf1718 <- subset(hf_all, year2=="2017/2018")
hf1718_g <- ggplot(hf1718, aes(Peak_Freq, fill=Region)) +
  geom_histogram(color="black", alpha=0.5, bins=15) +
  theme_bw() +  
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        plot.title = element_text(size = 18, family = "Times", face = "bold"),
        text=element_text(family = "Times"),
        axis.title = element_text(face="bold", size = 20),
        axis.title.y = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x = element_text(colour="black", size = 20, vjust=0.5),
        axis.text.y = element_text(colour="black", size = 20), legend.position = "none", 
        legend.text=element_text(size=20), legend.title=element_blank()) +
  scale_x_continuous(limits=c(120,135)) +
  scale_fill_manual(values=c("darkviolet", "blue"))
hf1718_g

grid.arrange(hf1516_g, hf1617_g, hf1718_g,
             ncol = 1,
             widths = 2, 
             heights = c(2, 2, 2))

Supplementary Material

Wilcox Pairwise Comparisons !!

Test OBS - EARS Supplementary material: Test OBS (ocean bottom seismometer) - EARS (Ecologic Acoustic Recorders)

# Wilcox test for pairwise comparisons
library (coin)

## Loading required package: survival

library(rstatix) #loading required packages

## 
## Attaching package: 'rstatix'

## The following objects are masked from 'package:coin':
## 
##     chisq_test, friedman_test, kruskal_test, sign_test, wilcox_test

## The following object is masked from 'package:stats':
## 
##     filter

test <- read.csv(file="~/Desktop/DATA/test_ear_obs.csv", header=TRUE, dec=".")
median(test$INI_ear)

## [1] 16.42

median (test$INI_obs)

## [1] 16.527

median(test$Peak_freq_ear)

## [1] 23.4

median (test$Peak_freq_obs)

## [1] 21.1

ini_test <- wilcox.test(test$INI_ear, test$INI_obs, paired=TRUE)
ini_test

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  test$INI_ear and test$INI_obs
## V = 11605, p-value = 0.4703
## alternative hypothesis: true location shift is not equal to 0

freq_test <- wilcox.test(test$Peak_freq_ear, test$Peak_freq_obs, paired=TRUE)
freq_test

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  test$Peak_freq_ear and test$Peak_freq_obs
## V = 16419, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0

CARLY ADD INTERPRETATION HERE

Figure Supplement 2

• Extension of Figure 3: Averaged peak frequencies of the 20-Hz notes per song from all data

# Averaged peak frequencies of the 20-Hz notes per song from all data
bout99 <- read.csv(file="~/Desktop/DATA/PF_ave_all.csv", header=TRUE, dec=".")
bout99$date <- as.Date(bout99$date, format = "%d/%m/%Y")

freq20_b <- ggplot(bout99, aes(x = date, y = Ave_PF, color = Region, shape = Recorder)) +
  geom_point(size = 2, bg = "black", lwd = 2, aes(group = 1)) +
  geom_errorbar(aes(ymin = Ave_PF - SD_PF, ymax = Ave_PF + SD_PF), width = 30) +
  scale_y_continuous(name = "Average song \n20-Hz note peak frequency (Hz)",
                     limits = c(18, 27)) +
  scale_x_date(breaks = "1 year", date_labels = "%Y") +
  scale_color_manual(values = c("darkviolet", "darkorange", "green4", "black", "red", "turquoise", "darkblue")) +
  theme_bw() +
  theme(
    plot.title = element_text(size = 12, family = "Times", face = "bold"),
    text = element_text(family = "Times"),
    panel.grid.major.y = element_line(colour = "black", linetype = 2),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_line(colour = "black", linetype = 2),
    strip.text.x = element_text(size = 12, face = "bold",
                                margin = margin(0.5, 0, 0.5, 0, "cm")),
    strip.background = element_rect(fill = "grey97"),
    axis.title = element_text(face = "bold", size = 12),
    axis.title.x = element_blank(),
    axis.text.x = element_text(colour = "black", size = 12,
                               vjust = 0.5, angle = 90),
    axis.text.y = element_text(colour = "black", size = 12),
    legend.position = "bottom",
    legend.box = "horizontal",
    legend.box.just = "center",
    legend.text = element_text(size = 10),
    legend.title = element_blank(),
    legend.spacing.x = unit(0.3, 'cm'),
    legend.spacing.y = unit(0.2, 'cm')
  ) +
  guides(
    color = guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size = 3)),
    shape = guide_legend(nrow = 1, override.aes = list(size = 3))
  )
print(freq20_b)

CHANGES TO HISTOGRAMS: figure out a way to make the plots themselves larger in the combined image - final check - sent to j and make sure that he can run this on his computer - make notes collapseable so you’re taking up less space in final presentation - label all figures A B etc like u did with figure 1 - include introduction information / brief methods section - change color of drop down boxes to white to make them easier to see - End with your critiques and opinions of the overall paper - add interpretation for wilcox test - break up figure 3 into figure 3 a b c sections second set of histograms for figure 4 - make axes sizes smaller

• data was incredibly organized